Field scale optimization is known which attempts to optimize or enhance the production of production fluids, including hydrocarbons, from a field containing one or more subterranean reservoirs. Wells or well bores connect the reservoirs with surface facilities which collect and process the captured production fluids. Typically, these production fluids include the components of oil, gas and water. Chokes or flow control devices are used to adjust the allocation of flow rates among the well bores in a field. The relative quantities and ratios of production of the different components of oil, gas and water for an individual well bore can be controlled by adjusting a choke to change the pressure in a well bore.
Surface facilities are needed to produce and process the production fluids. These facilities may include apparatus such as separators, pumps, storage tanks, compressors, etc. Ideally, the capital expenditures on these facilities are minimized by employing the smallest and least expensive surface facilities possible. However, fluid handling capacity should be sufficiently large so as not to unduly limit the production rate of the economically desirable oil and/or gas. Hence, the allocation of fluid flow in the well bores is ideally optimized to maximize monetary return while meeting production constraints such as those imposed by the fluid handling capacities of the surface facilities.
Optimization techniques are used predict the optimal allocation of fluid flows in well bores for a given set of production constraints. First, a reservoir simulator is used to mathematically model the flow of fluids throughout a field including the reservoirs and well bores. The simulated flow is used to establish component flow rate curves or rate equations for each well bore which describe how the flow rate of one component, such as water, relates to the flow rate of another component, i.e., oil. Typically, an objective function is created which seeks to optimize an objective such as maximizing oil production or minimizing water production. The objective function incorporates the flow rates from the well bores which are predicted by the reservoir simulation. A set of production constraints, such as oil production targets or gas or water production limitations for the field, are specified. Constraint equations are generated to meet these production constraints. The fluid flow among the well bores must adhere to these production constraints. The objective function is then optimized by a subroutine, referred to as an optimizer, to determine the optimal allocation of flow rates among the well bores. The optimizer utilizes the well bore component flow rate equations and constraint equations in the optimization process.
A first shortcoming of typical field scale optimization schemes is that feasible solutions to an optimization may not be possible for specified production constraints. For example, a certain level of oil production may be desired while not producing more than a specified quantity of water. A feasible solution to the objective function with this set of constraints may not be possible. In this event, one or more of the constraints must be adjusted and the reservoir simulator and optimizer run again to determine when a feasible solution is possible. Such iterative runs in solving numerous optimizations of the objective function are computationally intensive and undesirable.
A second problem in some optimization schemes is that while a feasible solution to the optimization of the objective function may be achieved, the results may not be practical. For example, in a first run or time step, the optimizer may determine that a first well bore should produce at a high level while a second well bore is substantially closed down. In the next time step, the optimizer may suggest that the second well bore produce at a high level while the first well bore is substantially shut down. Therefore, production from the well bores may oscillate if the suggested allocations from the optimizer are followed. Generally, it is more practical if the production from well bores having similar fluid flow characteristics are at a consistent level. This would minimize the oscillations in production from the related well bores over time steps.
A third shortcoming is that creating component flow rate curves or equations for the production of fluids from a well bore can be computationally intensive. One method of calculating these rate curves is to create a sub model of the well bores and surrounding reservoirs and iteratively solve for the production rates of the components, i.e., oil, gas and water, as the chokes are opened and the pressure draw downs between the reservoirs and the well bores are increased. Typically, several Newton iterations must be performed to produce each data point relating the production of one component relative to another component for a given pressure draw down in a well bore. Again, the pressure draw down in a well bore is related to how open is a choke controlling the well bore. This process is repeated many times until enough data points, perhaps as many as 30-50 data points, have been calculated such that an overall flow rate curve or equation can be developed. The optimizer then uses the rates curves or equations during the optimization of the objective function. Generating data points using these many Newton iterations to create rate curves or equations is computationally costly.
The present invention provides solutions to the above described shortcomings of conventional field scale optimization schemes. First, an objective function and associated constraint equations are generated which can be solved in a single run of an optimizer to produce a feasible solution. Second, constraint equations may be created which requires the rates of production from similar well bores to be related to prevent significant oscillation of well rates between time steps of a reservoir simulation. Finally, an efficient method of generating well bore component flow rate curves or equations relating production rates between fluid components of a well bore is described.